Targeting and data collection techniques provide advertisers and other marketing organizations with market segment data related to advertising viewers, including, for example, computer users who view advertising on the World Wide Web (Web) or Internet. For advertising viewers such as Internet users, the available information related to each user depends, for example, on his or her historical Web behavior and, for example, on his or her origin environment, such as the user's computing platform, service provider, country, time of day, etc. A “market segment” or “segment” is a subset, or partial portion of a group that can be characterized in some way; a segment may also be a data object describing such a group.
Advertisers and other marketing organizations may create segment definitions to define groups of potential marketing targets (e.g., users) and direct advertising to those groups, such as groups of users on the Internet. “Data publishers” (or “data sellers”) may sell information concerning targets or people, such as Internet users, and their behaviors, which advertisers and other marketing organizations may use to create, for example, behavioral segment definitions. An Internet user may access a Web site of a data publisher, such as a bicycling interest Web site, for example, and be identified as a user “interested in bicycling.” Other attributes, such as time and location of the person's access, may also be identified. Data publishers may sell the identifying information about users who access their sites and receive income from sales based on this information's use.
User identification (ID) data from data publishers can be used to create segment definitions. In general, segment definitions may be characterized by specific values for available properties. For example, segment definitions might exist for categories such as “Gender”, “Age” and “Nationality” and one segment combination might be defined with three properties as, “Male, 35-40, European.” Once identified (e.g., from information from a data publisher (data seller)), a user who fits the characteristics of “Male, 35-40, European” can be grouped into and/or associated with this segment combination. An advertisement can be exposed to (or placed) with users identified with the segment combination, and data can be collected to determine how the users identified with that segment respond. Behavioral segment definitions for “Shopping Interest”, “Running Interest” and “Web surfing interest” can be defined and Behavioral attributes, such as “likes to shop”, “intensely likes running” or “Web surfs in the evening” can also be included in segment combinations. Segment combinations can have attributes that are purely behavioral, purely non-behavioral or a mixture of behavioral and non-behavioral.
The efficiency of a given advertisement depends on the match between the content of the advertisement (advertising content) and the market segment to which the content is exposed. In practice, a numeric “conversion ratio” value describes the efficiency or “success” relationship between the advertising content and target segment. A high conversion ratio value can show, for example, by various measures or various methods of determining or collecting such data, that a given advertisement or advertising campaign (group of advertisements) is well received by a given target segment.
It is perceived within the advertising and marketing industries that, in general, better and more accurate segment targeting capabilities could improve conversion ratios. High conversion ratios for advertisements, on the Internet and in other advertising venues, such as, e.g., print, outdoor, direct are desirable. Identification, for example, of a large user group with a high response rate to advertising and with members who respond in stable and predictable manners over time is desirable.
Within Internet marketing, serving systems for organizations executing advertisement placement in advertising campaigns may execute “media optimization” when placing an advertisement on a particular Web site or with a particular media publisher. Media optimization may include analyzing parameters in segment combinations to identify values for each parameter that may yield the “best results” for each advertisement the serving system runs. A serving system may be a networked computing system that enables an operator to place advertisements on particular Web pages. Serving systems place advertisements on behalf of an advertiser or advertising agency, and can be operated by a number of entities such as an independent operator working with an advertiser or advertising agency.
One approach to Internet marketing uses association algorithms to create set of rules. An association algorithm is an algorithm for learning association rules. Association algorithms attempt to find subsets that are common to at least a minimum number of item sets. Association algorithms typically use a “bottom up” approach, in which frequent subsets are extended one item at a time to create candidates, and successful groups of candidates are tested against the data. The algorithm terminates when no further successful extensions are found. Many software implementations of different forms of association algorithms exist. For example, the “arules” package for the R Project for Statistical Computing provides the an implementation for representing, manipulating and analyzing transaction data and patterns, including frequent item sets and association rules. The package also provides interfaces to implementations of the association mining algorithms apriori and Eclat by C. Borgelt.
The rules may be useful for determining whether a user is likely to respond to a an advertisement given one or more attributes. However, as number of attributes increases, generating more complex association rules becomes increasingly difficult and computationally prohibitive. For example, when developing rules based on 5 attributes out of a total of 10,000 attributes, over 8.325×1017 combinations are possible.
With the development of the Internet advertising market, information about the people most likely to visit a website and information about the people most likely to purchase a product from visiting a website is increasingly more valuable. These people may be classified into modeled audience extensions, which defines segments that define users most likely to take certain actions. More accurate and efficient identification of modeled audience extensions can lead to more conversions and better return on investment for advertising money spent.
Serving systems using available media optimization algorithms have distinct limitations. First, segment content does not get improved with time, because systems do not allow for improvements as more data is amassed. Second, current systems cannot place people into segments that define the most likely people to visit a website or most likely people to purchase a product from visiting a website without a tremendous amount of pre-processing. Third, it is incredibly difficult and sometimes computationally impossible to develop effective sets of rules based on association algorithms. Fourth, association rules do not always deliver the required minimum number of likely responders when applied to a dataset over a given period of time, and combining rules often results in situations where the rules have a lower lift because they often have a large number of disparate members. In these situations, the separate rules may have both overlapping responders and overlapping non-responders, but the combined rule only increases the number of non-responders. Additionally, current systems do not provide adequate centralization, in which behavioral aspects from a wide group of data publishers provide an advantage in manipulating a wide variety of data and could enhance segment groupings.
In general, there is a need for improved techniques for selecting association rules when determining potential responders, in the advertising and marketing fields in general and, in particular, with regard to Internet advertising.